Abstract: An autonomous driving controller includes: a processor to collect driving data when a vehicle is traveling and calculate a steering override reference value, which is a criterion of determining an override mode, based on the collected driving data; and a storage to store the collected driving data and a set of instructions executed by the processor to calculate the steering override reference value. In particular, the processor controls autonomous driving by varying the steering override reference value based on the collected driving data or information regarding a driver of the vehicle.

Abstract: Extrinsic calibration of a Light Detection and Ranging (LiDAR) sensor and a camera can comprise constructing a first plurality of reconstructed calibration targets in a three-dimensional space based on physical calibration targets detected from input from the LiDAR and a second plurality of reconstructed calibration targets in the three-dimensional space based on physical calibration targets detected from input from the camera. Reconstructed calibration targets in the first and second plurality of reconstructed calibration targets can be matched and a six-degree of freedom rigid body transformation of the LiDAR and camera can be computed based on the matched reconstructed calibration targets. A projection of the LiDAR to the camera can be computed based on the computed six-degree of freedom rigid body transformation.

Abstract: In one embodiment, a method includes accessing a set of data points captured using a radar system of the vehicle. Each data point is associated with at least three measurements include a Doppler measurement, a range measurement, and an azimuth measurement in reference to the radar system. The method also includes clustering the set of data points into one or more first clusters based on a first pair of the three measurements associated with each of the data points; and clustering the set of data points into one or more second clusters based on a second pair of the three measurements associated with each of the data points. The second pair being different from the first pair of the three measurements.

Abstract: Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.

Abstract: Systems and methods for controlling an autonomous vehicle to reduce idle data usage and vehicle downtime are provided. In one example embodiment, a computing system can obtain data associated with autonomous vehicle(s) that are online with a service entity. The computing system can obtain data indicative of the geographic area with an imbalance in a number of vehicles associated with the geographic area. The computing system can determine a first autonomous vehicle for re-positioning with respect to the geographic area based at least in part on the data associated with the one or more autonomous vehicles and the data indicative of the geographic. The computing system can communicating data indicative of a first re-positioning assignment associated with the first autonomous vehicle. In some implementations, the computing system can generate vehicle service incentive to entice a vehicle provider to re-position its autonomous vehicles with respect to the geographic area.

Abstract: A brake system for a transportation vehicle, a transportation vehicle having a brake system, and a method for operating a brake system. The brake system has two control units, wherein the respective control unit actuates a respective brake circuit of the brake system, which includes two of four service brakes and one of two electric parking brakes of the brake system. In response to a defect in one of the brake circuits, the control unit of the other brake circuit actuates the respective brakes of the other brake circuit, to carry out trailer combination stabilization of a trailer combination having the transportation vehicle and a trailer coupled to the transportation vehicle; and/or to steer the transportation vehicle in the case of a defect in a steering system of the transportation vehicle based on a steering command of a control device for autonomous driving.

Abstract: Provided is a survey system capable of more highly accurately obtaining a product of a three-dimensional survey. A survey system includes a mobile body, a scanner including an emitting unit, a light receiving unit, a distance measuring unit, a first optical axis deflecting unit disposed on an optical axis of the distance measuring light and configured to deflect a distance measuring light, a second optical axis deflecting unit disposed on a light receiving optical axis of the reflected distance measuring light and configured to deflect a reflected distance measuring light at the same angle in the same direction as those of the first optical axis deflecting unit, and an emitting direction detecting unit to detect deflection angles and directions of the first and the second optical axis deflecting units, a posture detecting device of the scanner, and a position measuring device of the scanner.

Abstract: Systems and methods for controlling an autonomous vehicle to reduce idle data usage and vehicle downtime are provided. In one example embodiment, a computing system can obtain data associated with autonomous vehicle(s) that are online with a service entity. The computing system can obtain data indicative of the geographic area with an imbalance in a number of vehicles associated with the geographic area. The computing system can determine a first autonomous vehicle for re-positioning with respect to the geographic area based at least in part on the data associated with the one or more autonomous vehicles and the data indicative of the geographic. The computing system can communicating data indicative of a first re-positioning assignment associated with the first autonomous vehicle. In some implementations, the computing system can generate vehicle service incentive to entice a vehicle provider to re-position its autonomous vehicles with respect to the geographic area.

Abstract: An automated driving control unit sets an automated driving state to a first automated driving state (step S3), in a case that an automated driving instruction unit has instructed the initiation of automated driving in a state in which a destination is set by a destination setting unit, sets the automated driving state to a second automated driving state (step S4), in a case that the automated driving instruction unit has instructed the initiation of automated driving in a state in which the destination is not set by the destination setting unit, and causes the automated driving state to transition from the first automated driving state to the second automated driving state (step S13), in a case that a current travel position lies outside of a travel route during the travel control in the first automated driving state.

Abstract: Various examples are directed to systems and methods for navigating an autonomous vehicle. Trip plan data may describe a plurality of candidate vehicle start points, a plurality of candidate waypoints, and a plurality of candidate vehicle end points. A plurality of candidate routes may be determined between an algorithm start point and an algorithm end point. Each candidate route of the plurality of candidate routes may include at least one of the plurality of candidate waypoints and at least one of the plurality of candidate vehicle end points. A best rate may be determined using the plurality of candidate routes. The best route may include a first candidate vehicle start point, a first candidate waypoint, and a first candidate vehicle end point. The autonomous vehicle may be controlled along the best route from the first candidate vehicle start point towards the first candidate vehicle end point.

Abstract: A georeferenced trajectory system for vehicles receives trajectory data generated by a plurality of vehicle sensors and scaffolds of previously generated maps and aligns geometry data for a geographic region and trajectory data from the received data from different map builds. A scaffold of a geographic region to be mapped during an initial map build is generated, and the trajectory data from respective map builds is aligned with the scaffold of previously generated maps to generate a map of the geographic region. The resulting map expands the coverage of the existing map such that old and new map data is in a common consistent reference frame whereby the map may be built incrementally by merging or expanding local scaffolds and filling in the merged or expanded scaffold while ensuring global consistency.

Abstract: A laser range finder projects light while changing a direction in a horizontal direction at a predetermined angle with respect to a vertical direction to also receive reflected light of the light, and detects a direction and a distance in which the light is reflected from an obstacle or the like, according to a difference time between a time of light projection and a time of light reception. A normal direction of a flat plane forming a road surface is detected on the basis of a polarized image. The laser range finder projects light such that the light has a predetermined angle with respect to the vertical direction so as to be orthogonal to the normal direction of the flat plane forming the road surface. The laser range finder can be applied to in-vehicle systems.

Abstract: An autonomous driving control device includes a map information input unit, a new map information storage unit, and a calculation unit. The map information input unit includes several input means and several processing means. The calculation unit performs the autonomous driving using the new map information obtained by accessing the new map information storage unit.

Abstract: Described herein are embodiments of a neural network-based task planner (TaskNet) for autonomous vehicle. Given a high-level task, the TaskNet planner decomposes it into a sequence of sub-tasks, each of which is further decomposed into task primitives with specifications. TaskNet comprises a first model for predicating the global sequence of working area to cover large terrain, and a second model for determining local operation order and specifications for each operation. The neural models may include convolutional layers for extracting features from grid map-based environment representation, and fully connected layers to combine extracted features with past sequences and predict the next sub-task or task primitive. Embodiments of the TaskNet are trained using an excavation trace generator and evaluate its performance using a 3D physically-based terrain and excavator simulator.

Abstract: A vehicle control device includes: a driving controller configured to perform first driving control such that one or both of an acceleration or deceleration speed and steering of a vehicle is controlled to travel the vehicle; an occupant intention detector configured to detect an intention of the occupant to switch from manual driving to a state in which the driving controller performs the first driving control; and a one-way traffic determiner configured to determine whether a road on which the vehicle is predicted to be traveling or travel in future is a one-way traffic road. The driving controller determines whether to start the first driving control based on whether the one-way traffic determiner determines the road on which the vehicle is predicted to be traveling or travel in future is the one-way traffic road.

Abstract: A computer-implemented method for intersection communication includes detecting a first vehicle behind a host vehicle and in a same lane as the host vehicle and determining a rear distance between a rear end of the host vehicle and a front end of the first vehicle. The method also includes transmitting a backup request to the first vehicle based on the rear distance and a wait time period between the stop state and a go state controlled by a traffic signal device. Further, the method includes controlling the host vehicle to perform a backup maneuver with respect to an intersection based on the rear distance and the wait time period.

Abstract: In the present invention, when a road surface condition is determined based on information acquired by a camera installed in a vehicle, a route of a host vehicle is predicted and the is determined. A road surface condition determination method and device determines a road surface condition of a predicted route based on information acquired by a camera installed in a host vehicle. A controller predicts a route of the host vehicle by determining a road surface friction coefficient of the predicted route based on information acquired by the camera. The determining of the road surface friction coefficient of the predicted route includes: dividing an ahead-of-vehicle image acquired by the camera in a left-right direction and determining a road surface condition for each of the determination areas, and determining the road surface friction coefficient in the determination areas through which the predicted route will pass.

Abstract: A vehicle control apparatus that controls a vehicle based on a plurality of control states, includes a detection unit configured to detect information of the vehicle and peripheral information of the vehicle and a vehicle control unit configured to control the vehicle based on a detection result of the detection unit. In a case in which the vehicle control unit can execute, based on the detection result of the detection unit, a first control state and a second control state, the vehicle control unit controls the vehicle by selecting a control state prioritizing one of the first control state and the second control state in an overlapping operation condition range.

Abstract: A system, method, and non-transitory computer-readable storage medium to perform a search and rescue mission according to a Layered Search and Rescue (LSAR) methodology using a plurality of Unmanned Aerial Vehicles (UAVs) communicatively connected to a remote server. The LSAR methodology can involve receiving data corresponding to a center of an area corresponding to an adverse/disaster event potentially having survivors at unknown locations; dividing the area into a set of numbered box-shaped layers within the area; calculating a thickness of the box-shaped layers based on a total number of the Unmanned Aerial Vehicles; exclusively assigning one or more of the Unmanned Aerial Vehicles to each box-shaped layer; and controlling the Unmanned Aerial Vehicles to perform the search and rescue mission by selectively switching one or more of the Unmanned Aerial Vehicles between a searcher mode and a rescuer mode.

Abstract: A drawing apparatus is connected to a compound design apparatus. The design apparatus includes at least a display for displaying a drawing, a data base device for storing therein reference data to analyze the drawing, a storage for storing therein analysis data of the drawing, and a computing unit connected to the drawing apparatus to analyze the drawing. The computing unit may include a drawing evaluating section for extracting a graphic image of a particular mechanism part from the drawing generated by the drawing apparatus and for evaluating the graphic image, an analysis model generator for generating, according to a profile of the graphic image, an analysis model having a mechanism equivalent to the graphic image, an analyzer for reading mechanical characteristic data from the data base device, supplying the data to the analysis model, and analyzing the mechanism, and an analysis evaluating section for evaluating analysis results attained from the analyzer.